Bibliographic record
Abstract
Despite extensive literature on falls among seniors, little is known about gender-specific risk factors. To determine the prevalence of falls by gender and sociodemographic, lifestyle/behavioral, and medical factors, we conducted a cross-sectional study in a nationally representative sample of Canadian adults who were 65 years of age or older (n = 14,881) from the Canadian Community Health Survey-Healthy Aging (2008-2009). Logistic regression models were applied to investigate gender-specific associations between potential risk factors and falls. In men, stroke (odds ratio (OR) = 1.91), nutritional risk (OR = 1.86), post-secondary school degree (OR = 1.68), eye disorder (OR = 1.35), widowed/separated/divorced marital status (OR = 1.28), and arthritis (OR = 1.27) were independently associated with significantly higher odds of falls. In women, significant independent correlates of falls included stroke (OR = 1.53), age of 85 years or older (OR = 1.51), nutritional risk (OR = 1.39), consumption of at least 1 alcoholic drink per week (OR = 1.39), use of 5 or more medications (OR = 1.36), arthritis (OR = 1.36), diabetes (OR = 1.31), and osteoporosis (OR = 1.22). Higher physical activity levels were protective in both genders, and higher household income was protective in women. Gender should be considered when planning fall prevention strategies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".